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Mapping Two Decades of Autonomous Vehicle Research: A Systematic Scientometric Analysis

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  • Asif Faisal
  • Tan Yigitcanlar
  • Md. Kamruzzaman
  • Alexander Paz

Abstract

Autonomous vehicles (AV) have become a symbol of futuristic and intelligent transport innovation. This new driving technology has received heightened attention from academic, public, and private sectors. Nonetheless, a big challenge limiting a clear understanding of AV research is its scale. A large volume of literature is produced—covering various fields. This paper aims to map out the research on AV for a better understanding of the trends, patterns, and interconnections, and it critically reflects on their implications for research. A scientometric analysis technique is applied to analyze 4,645 papers published between 1998 and 2017. The findings disclose that (a) 87.7 percent of the AV studies was conducted by educational institutes; (b) Europe is the most productive continent in AV research with a 35.9 percent share of publications; (c) North America is the most influential continent in AV research, receiving 41.1 percent of the citations; (d) Over 50 percent of the studies were conducted during the last three years of the analysis period; (e) Urban and social contexts of AV research are still at their early stage; and (f) Relatively limited collaboration and knowledge sharing between academia and industry exist.

Suggested Citation

  • Asif Faisal & Tan Yigitcanlar & Md. Kamruzzaman & Alexander Paz, 2021. "Mapping Two Decades of Autonomous Vehicle Research: A Systematic Scientometric Analysis," Journal of Urban Technology, Taylor & Francis Journals, vol. 28(3-4), pages 45-74, October.
  • Handle: RePEc:taf:cjutxx:v:28:y:2021:i:3-4:p:45-74
    DOI: 10.1080/10630732.2020.1780868
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    Citations

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    Cited by:

    1. Tan Yigitcanlar, 2022. "Towards Smart and Sustainable Urban Electromobility: An Editorial Commentary," Sustainability, MDPI, vol. 14(4), pages 1-7, February.
    2. Jang, Hyejin & Lee, Suyeong & Yoon, Byungun, 2023. "Data-driven techno-socio co-evolution analysis based on a topic model and a hidden Markov model," Technovation, Elsevier, vol. 126(C).
    3. Jen Sim Ho & Booi Chen Tan & Teck Chai Lau & Nasreen Khan, 2023. "Public Acceptance towards Emerging Autonomous Vehicle Technology: A Bibliometric Research," Sustainability, MDPI, vol. 15(2), pages 1-18, January.
    4. Du, Manqing & Zhang, Tingru & Liu, Jinting & Xu, Zhigang & Liu, Peng, 2022. "Rumors in the air? Exploring public misconceptions about automated vehicles," Transportation Research Part A: Policy and Practice, Elsevier, vol. 156(C), pages 237-252.
    5. Faisal, Asif & Yigitcanlar, Tan & Paz, Alexander, 2023. "Understanding driverless car adoption: Random parameters ordered probit model for Brisbane, Melbourne and Sydney," Journal of Transport Geography, Elsevier, vol. 110(C).
    6. Istiak Ahmad & Fahad Alqurashi & Ehab Abozinadah & Rashid Mehmood, 2022. "Deep Journalism and DeepJournal V1.0: A Data-Driven Deep Learning Approach to Discover Parameters for Transportation," Sustainability, MDPI, vol. 14(9), pages 1-72, May.
    7. Agrawal, Shubham & Schuster, Amy M. & Britt, Noah & Mack, Elizabeth A. & Tidwell, Michael L. & Cotten, Shelia R., 2023. "Building on the past to help prepare the workforce for the future with automated vehicles: A systematic review of automated passenger vehicle deployment timelines," Technology in Society, Elsevier, vol. 72(C).
    8. Li, Wenda & Yigitcanlar, Tan & Liu, Aaron & Erol, Isil, 2022. "Mapping two decades of smart home research: A systematic scientometric analysis," Technological Forecasting and Social Change, Elsevier, vol. 179(C).
    9. Nader Zali & Sara Amiri & Tan Yigitcanlar & Ali Soltani, 2022. "Autonomous Vehicle Adoption in Developing Countries: Futurist Insights," Energies, MDPI, vol. 15(22), pages 1-26, November.

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